Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Accurate estimation and robust modelling of translation dynamics at codon resolution

Movie Recommendation System using the MovieLens dataset

Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best)

glmnet for python

For quick search

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data

Applied Machine Learning

Use Ridge Regression and Lasso Regression in prostate cancer data

Implementation of some Machine Learning Algorithms from scratch

Python implementations of semiparametric statistical techniques.

The Cancer Genome Atlas Pan-Cancer analysis project

OLS (twoway clustered standard errors), Imperfect Multicollinearity (Ridge and PCA), ARMA(p,q) with Bootstrap

Machine Learning Project to predict FWI value using Ridge Regression

This repository implements a rolling window framework using Ridge Regression to adaptively model and forecast financial time series. The code also evaluates model accuracy through Mean Squared Error (MSE) and Mean Absolute Error (MAE) across varying window sizes, with detailed visualizations of prediction performance.

Here I upload my ML test scripts written in MATLAB or Python

This toolbox offers 7 machine learning methods for regression problems.